1. Field of the Invention
The present invention is directed to computer-assisted systems which automatically recognize an input pattern such as a picture image, voices and characters, and relates particularly to a pattern recognition apparatus which recognizes handwritten character patterns using a structural analysis method.
2. Description of the Related Art
Today, various methods have been developed as automatic pattern recognition methods for handprinted characters. A structural analysis method executes pattern recognition by detecting a contour pattern of an input character, dividing the detected contour line into a plurality of partial patterns (normally called "segments") and searching the structure of the input character based on the shapes or characteristics of these partial patterns. Such a method is effective in recognizing, with high accuracy, strongly distorted characters which are freely handwritten by people.
A pattern recognition apparatus employing a structural analysis method uses, as reference data (reference segments), characteristic data of standard segment shapes that are prepared by executing a statistical analysis on standard contour patterns of characters registered for the individual categories. The reference segments are significant in characterizing the shape of a character. These reference segments are registered in a main reference section. To recognize the pattern of an input character, first, the contour pattern of this input character is detected, followed by segmentation of the detected contour pattern, thereby providing partial contour segments of the input character pattern. Each contour segment is sequentially compared, and collated, with segments corresponding to one reference character pattern by a matching unit. When all the segments of one input character are matched with all the reference segments of a reference character, pattern recognition for the input character is said to be successful. In the matching process, if the input does not uniquely match with a single reference, the next reference character pattern will be selected and comparison and collation between the input and the selected pattern will be carried out; this process is repeated until pattern matching succeed.
In the reference section of the pattern recognition apparatus, registered reference characters are typically classified into several categories. For instance, English characters are generally classified into alphabets and numerals. With such a simple reference structure, however, if an input character pattern is intricate, it may be undesirably and erroneously matched with a reference character that belongs to a different category, but yet has a high similarity in shape. This impairs the accuracy of pattern recognition. Given that an input character is "1" (alphanumeral), if it is discriminated to be matched with a reference character belonging to a different category, such as "7" (also an alphanumeral), due to their similarity in shape, the pattern recognition process for the input would fail. Failure in pattern recognition of a handwritten character pattern deteriorates the accuracy of the recognition result and impairs the reliability of this pattern recognition process.
As a solution to this problem, there is an attempt or measure to additionally describes a detailed recognition program for each reference character in the reference section in association with each segment. More specifically, each reference segment of each reference character is added with detailed sub-data such as the length, curvature and positional relationship between adjoining segments. In comparing and collating an input character pattern with reference characters, when the input is discriminated to be matched with reference characters of several different categories through a main pattern matching process, a detailed pattern recognition process is executed using the detailed recognition program to thereby determine which one of the probable categories is correct.
With such an arrangement, however, the reference data structure is very intricate and rewriting or updating the contents of the reference is not an easy task for operators. For instance, in updating reference segment data of one reference character, the detailed recognition program added to each segment of that reference character should also be updated every time. Such an updating work is troublesome to operators and it is significantly difficult to self-manage the correspondence or corresponding relationship between old and new detailed recognition programs existing respectively before and after updation.